most citedA Survey of AI Agent Protocols

8 citations · 11 across the 5 of their papers we have counts for

collaborators

6 papers

cs.AI20262 cited

Understanding Agent Scaling in LLM-Based Multi-Agent Systems via Diversity

Yingxuan Yang, Chengrui Qu, Muning Wen +5

LLM-based multi-agent systems (MAS) have emerged as a promising approach to tackle complex tasks that are difficult for individual LLMs. A natural strategy is to scale performance…

stat.ML2026

An Efficient Algorithm for Thresholding Monte Carlo Tree Search

Shoma Nameki, Atsuyoshi Nakamura, Junpei Komiyama +1

We introduce the Thresholding Monte Carlo Tree Search problem, in which, given a tree and a threshold , a player must answer whether the root node value of $\mathc…

cs.CL2025

PARL-MT: Learning to Call Functions in Multi-Turn Conversation with Progress Awareness

Huacan Chai, Zijie Cao, Maolin Ran +11

Large language models (LLMs) have achieved impressive success in single-turn function calling, yet real-world applications such as travel planning or multi-stage data analysis typi…

cs.AI20251 cited

Agentic Web: Weaving the Next Web with AI Agents

Yingxuan Yang, Mulei Ma, Yuxuan Huang +15

The emergence of AI agents powered by large language models (LLMs) marks a pivotal shift toward the Agentic Web, a new phase of the internet defined by autonomous, goal-driven inte…

cs.AI2025

Agent Exchange: Shaping the Future of AI Agent Economics

Yingxuan Yang, Ying Wen, Jun Wang +1

The rise of Large Language Models (LLMs) has transformed AI agents from passive computational tools into autonomous economic actors. This shift marks the emergence of the agent-cen…

cs.AI20258 cited

A Survey of AI Agent Protocols

Yingxuan Yang, Huacan Chai, Yuanyi Song +11

The rapid development of large language models (LLMs) has led to the widespread deployment of LLM agents across diverse industries, including customer service, content generation,…